US2023376758A1PendingUtilityA1
Multi-modality root cause localization engine
Est. expiryMay 20, 2042(~15.8 yrs left)· nominal 20-yr term from priority
G06N 3/04G06N 20/00G06N 3/045G06N 3/0455G06N 3/08G06N 3/0442G06N 7/01G06F 11/0709G06F 11/0769G06F 11/079G06F 21/552G06F 21/577G06F 2221/034G06F 2221/2101G06N 3/088G06N 3/047G06N 3/048G06N 20/10G06N 3/042G06N 5/041
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Claims
Abstract
A method for employing root cause analysis is presented. The method includes embedding, by an embedding layer, a sequence of events into a low-dimension space, employing a feature extractor and representation learner to convert log data from the sequence of events to time series data, the feature extractor including an auto-encoder model and a language model, and detecting root causes of failure or fault activities from the time series data.
Claims
exact text as granted — not AI-modifiedWhat is claimed is:
1 . A method for employing root cause analysis, the method comprising:
embedding, by an embedding layer, a sequence of events into a low-dimension space; employing a feature extractor and representation learner to convert log data from the sequence of events to time series data, the feature extractor including an auto-encoder model and a language model; and detecting root causes of failure or fault activities from the time series data.
2 . The method of claim 1 , wherein the auto-encoder model includes a long short-term memory (LSTM) encoder network and an LSTM decoder network.
3 . The method of claim 2 , wherein the LSTM encoder network learns a representation of a whole sequence as follows:
f t =σ g ( W f x t i +U f h t−1 +b f )
i t =σ g ( W i x t i +U i h t−1 +b i )
o t =o g ( W o x t i +U o h t−1 +b o ) c t =tanh( W c x t +U c h t−1 +b c ) c t =f t ⊙c t−1 +i t +c t h t =o t ⊙c t , where x t is an input embedding of the t th element in a training set S i , f t , i t , o t are a forget gate, an input gate, and an output gate, respectively, W * , U * , and b * (*∈{f, i, o, c}) are all trainable parameters of the LSTM, and h N i is a final hidden state obtained by the LSTM.
4 . The method of claim 2 , wherein the LSTM decoder network reconstructs the whole sequence as follows:
h t i =LSTM( h t−1 ,x t−1 i ) p t i =Softmax(ReLU( W p h t i +b p )) e t i =OneHot(argmax( p t i ) x t i =E T ·e t i , where p t i ∈R |E| is a probability distribution over all possible events, W p and b p are trainable parameters, argmax is a function to obtain an index of largest entry of p t i , Softmax normalizes the probability distribution, ReLU is an activation function defined as ReLU(x)=max(0,x),e t i is a predicted event at step t, and h N i represents hidden states.
5 . The method of claim 2 , wherein parameters of the LSTM encoder network and the LSTM decoder network are optimized by an objective function defined as:
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where e j i T is a predicted event and p j i is a probability distribution over all possible events.
6 . The method of claim 1 , wherein the language model is trained to predict a next event given previous events in a categorical sequence.
7 . The method of claim 6 , wherein the next event is predicted as:
h t i =LSTM(( x 1 i ,x 2 i , . . . ,x t i )) p t+1 i =Softmax(ReLU( W l h t i +b l )) e t+1 i =OneHot(argmax( p t+1 i )), where p t+1 i a probability distribution over all possible event, e t+1 i is a one-hot representation the predicted next event, Softmax normalizes the probability distribution, ReLU is an activation function defined as ReLU(x)=max(0,x), argmax is a function to obtain an index of largest entry of p t i , W l and b l are trainable parameters, and h t i represent hidden states.
8 . A non-transitory computer-readable storage medium comprising a computer-readable program for employing root cause analysis, wherein the computer-readable program when executed on a computer causes the computer to perform the steps of:
embedding, by an embedding layer, a sequence of events into a low-dimension space; employing a feature extractor and representation learner to convert log data from the sequence of events to time series data, the feature extractor including an auto-encoder model and a language model; and detecting root causes of failure or fault activities from the time series data.
9 . The non-transitory computer-readable storage medium of claim 8 , wherein the auto-encoder model includes a long short-term memory (LSTM) encoder network and an LSTM decoder network.
10 . The non-transitory computer-readable storage medium of claim 9 , wherein the LSTM encoder network learns a representation of a whole sequence as follows:
f t =σ g ( W f x t i +U f h t−1 +b f )
i t =σ g ( W i x t i +U i h t−1 +b i )
o t =σ g ( W o x t i +U o h t−1 +b o )
c t =tanh( W c x t +U c h t−1 +b c ) c t =f t ⊙c t−1 +i t +c t h t =o t ⊙c t , where x t is an input embedding of the t th element in a training set S i , f t , i t , o t are a forget gate, an input gate, and an output gate, respectively, W * , U * , and b * (*∈{f,i,o,c}) are all trainable parameters of the LSTM, and h N i is a final hidden state obtained by the LSTM.
11 . The non-transitory computer-readable storage medium of claim 9 , wherein the LSTM decoder network reconstructs the whole sequence as follows:
h t i =LSTM( h t−1 ,x t−1 i ) p t i =Softmax(ReLU( W p h t i +b p )) e t i =OneHot(argmax( p t i ) x t i =E T ·e t i , where p t i ∈R |E| is a probability distribution over all possible events, W p and b p are trainable parameters, argmax is a function to obtain an index of largest entry of p t i , Softmax normalizes the probability distribution, ReLU is an activation function defined as ReLU(x)=max(0,x),e t i is a predicted event at step t, and h N i represents hidden states.
12 . The non-transitory computer-readable storage medium of claim 9 , wherein parameters of the LSTM encoder network and the LSTM decoder network are optimized by an objective function defined as:
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N
∑
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1
N
i
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log
(
e
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iT
p
j
i
)
,
where e j i T is a predicted event and p j i is a probability distribution over all possible events.
13 . The non-transitory computer-readable storage medium of claim 8 , wherein the language model is trained to predict a next event given previous events in a categorical sequence.
14 . The non-transitory computer-readable storage medium of claim 13 , wherein the next event is predicted as:
h t i =LSTM(( x 1 i ,x 2 i , . . . ,x t i )) p t+1 i =Softmax(ReLU( W l h t i +b l )) e t+1 i =OneHot(argmax( p t+1 i )) where p t+1 i a probability distribution over all possible event, e t+1 i is a one-hot representation the predicted next event, Softmax normalizes the probability distribution, ReLU is an activation function defined as ReLU(x)=max(0,x), argmax is a function to obtain an index of largest entry of p t i , W l and b l are trainable parameters, and h t i represent hidden states.
15 . A system for employing root cause analysis, the system comprising:
a processor; and a memory that stores a computer program, which, when executed by the processor, causes the processor to:
embed, by an embedding layer, a sequence of events into a low-dimension space;
employ a feature extractor and representation learner to convert log data from the sequence of events to time series data, the feature extractor including an auto-encoder model and a language model; and
detect root causes of failure or fault activities from the time series data.
16 . The system of claim 15 , wherein the auto-encoder model includes a long short-term memory (LSTM) encoder network and an LSTM decoder network.
17 . The system of claim 16 , wherein the LSTM encoder network learns a representation of a whole sequence as follows:
f t =σ g ( W f x t i +U f h t−1 +b f )
i t =σ g ( W i x t i +U i h t−1 +b i )
o t =σ g ( W o x t i +U o h t−1 +b o )
c t =tanh( W c x t +U c h t−1 +b c ) c t =f t ⊙c t−1 +i t +c t h t =o t ⊙c t , where x t is an input embedding of the t th element in a training set S i , f t , i t , o t are a forget gate, an input gate, and an output gate, respectively, W * , U * , and b * (*∈{f,i,o,c}) are all trainable parameters of the LSTM, and h N i is a final hidden state obtained by the LSTM.
18 . The system of claim 16 , wherein the LSTM decoder network reconstructs the whole sequence as follows:
h t i =LSTM( h t−1 ,x t−1 i ) p t i =Softmax(ReLU( W p h t i +b p )) e t i =OneHot(argmax( p t i ) x t i =E T ·e t i , where p t i ∈R |E| is a probability distribution over all possible events, W p and b p are trainable parameters, argmax is a function to obtain an index of largest entry of p Softmax normalizes the probability distribution, ReLU is an activation function defined as ReLU(x)=max(0,x),e t i is a predicted event at step t, and h N i represents hidden states.
19 . The system of claim 16 , wherein parameters of the LSTM encoder network and the LSTM decoder network are optimized by an objective function defined as:
L
A
E
=
∑
i
=
1
N
∑
j
=
1
N
i
-
log
(
e
j
iT
p
j
i
)
,
where e j i T is a predicted event and p j i is a probability distribution over all possible events.
20 . The system of claim 15 , wherein the language model is trained to predict a next event given previous events in a categorical sequence and wherein the next event is predicted as:
h t i =LSTM(( x 1 i ,x 2 i , . . . ,x t i )) p t+1 i =Softmax(ReLU( W l h t i +b l )) e t+1 i =OneHot(argmax( p t+1 i )), where p t+1 i a probability distribution over all possible event, e t+1 i is a one-hot representation the predicted next event, Softmax normalizes the probability distribution, ReLU is an activation function defined as ReLU(x)=max(0,x), argmax is a function to obtain an index of largest entry of p t i , W l and b l are trainable parameters, and h t i represent hidden states.Join the waitlist — get patent alerts
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